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Found 5 papers, 2 papers with code
Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
This paper proposes SAGD-IV, a novel framework for conducting nonparametric instrumental variable (NPIV) regression by employing stochastic approximate gradients to minimize the projected populational risk.
Price formation in financial markets: a game-theoretic perspective
We propose two novel frameworks to study the price formation of an asset negotiated in an order book.
KrigHedge: Gaussian Process Surrogates for Delta Hedging
We further discuss the application to Delta hedging, including a new Lemma that relates quality of the Delta approximation to discrete-time hedging loss.
Extensions of the Deep Galerkin Method
We extend the DGM algorithm to solve for the value function and the optimal control \simultaneously by characterizing both as deep neural networks.
Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning
In this work we apply the Deep Galerkin Method (DGM) described in Sirignano and Spiliopoulos (2018) to solve a number of partial differential equations that arise in quantitative finance applications including option pricing, optimal execution, mean field games, etc.
